© 2008 neha dixit
TRANSCRIPT
1
ATTENTIONAL BIAS IN PATIENTS WITH IMPLANTABLE CARDIOVERTER DEFIBRILLATORS: EXAMINING MECHANISMS OF HYPERVIGILENCE AND
ANXIETY
By
NEHA K. DIXIT
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2008
3
To my amazing grandparents whose value for learning, education and zestful spirit of adventure forged the way for higher education in the generations to come. And to my own parents and husband:
Your never ending support and love through this journey will always be with me. I am eternally grateful for each of you.
4
ACKNOWLEDGMENTS
I would like to thank my mentors, Bill Perlstein, Ph.D. and Samuel Sears, Ph.D., for their
support, supervision, and examination of what was really important throughout my graduate
school experience. You are both individuals who have made a permanent imprint on my mind,
heart and values. In addition, I would like to thank the members of the Clinical Cognitive
Neuroscience Lab and Cardiac Psychology Lab for their willingness to give assistance whenever
needed. I would like to especially thank Leann King RN, and Marcela Miranda, ARNP for their
efforts in aiding recruitment on this project. This research was supported by a pre-doctoral
fellowship from the Florida/Puerto Rico Affiliate of the American Heart Association to Neha K.
Dixit.
5
TABLE OF CONTENTS page
ACKNOWLEDGMENTS ...............................................................................................................4
LIST OF TABLES ...........................................................................................................................7
LIST OF FIGURES .........................................................................................................................8
ABSTRACT .....................................................................................................................................9
CHAPTER
1 INTRODUCTION ..................................................................................................................11
Overview and Study Aims ......................................................................................................11 Specific Aims ..........................................................................................................................12 Background and Significance .................................................................................................14
Psychosocial Effects of ICD Implantation ......................................................................16 Anxiety and the ICD Patient ............................................................................................16 Arrhythmias and Hypervigilance ....................................................................................17 Anxiety as Precipitant to Shock ......................................................................................18 Selective Attention ..........................................................................................................19 Attentional Bias and Emotion .........................................................................................20 Experimental Paradigms Examining Attentional Bias ....................................................22 Evidence of Attentional Bias in Anxiety Disorders ........................................................24
Significance ............................................................................................................................27
2 EXPERIMENTAL DESIGN AND METHODS ....................................................................29
Participants .............................................................................................................................29 Procedure ................................................................................................................................30
Shock Anxiety .................................................................................................................31 General Anxiety ...............................................................................................................31 General Health-Related Quality of Life ..........................................................................32 Depression .......................................................................................................................32 Cognitive Screener ..........................................................................................................33 Reading ............................................................................................................................33 Experimental Task ...........................................................................................................33
3 DATA ANALYSIS AND RESULTS ....................................................................................37
Data Analysis ..........................................................................................................................37 Dot Probe Task ................................................................................................................37 Reaction Time Data .........................................................................................................38 Error Data ........................................................................................................................38 Cue Word Valence and Arousal Ratings .........................................................................38
6
Cue Word Valence Ratings .............................................................................................39 Cue Word Arousal Ratings ..............................................................................................39
Effect of Shock on Dot Probe Task ........................................................................................39 Shock and Reaction Time ................................................................................................39 Shock and Error Rates .....................................................................................................39 Psychosocial Data ............................................................................................................40
4 DISCUSSION .........................................................................................................................45
Evidence of Attentional Bias ..................................................................................................45 Cohort Effects on Task ...........................................................................................................47 Differences in Methodology ...................................................................................................48 Manipulation Check ................................................................................................................49 Limitations ..............................................................................................................................50 Future Directions ....................................................................................................................51
APPENDIX: WORD STIMULI USED IN TASK ........................................................................52
REFERENCES ..............................................................................................................................53
BIOGRAPHICAL SKETCH .........................................................................................................59
7
LIST OF TABLES
Table page 2-1 Mean (+standard deviation) demographic and psychological test data for VF and AF
patients. ..............................................................................................................................36
A-1 Word stimuli used in task ..................................................................................................52
8
LIST OF FIGURES
Figure page 2-1 Example of a typical incongruent, clinically relevant trial. ...............................................36
3-1 Dot-probe task reaction times for VF (ICD) and AF (control) patients. ...........................42
3-2 Dot-probe task error rates for VF (ICD) and AF (control) patients. ..................................42
3-3 Subjective ratings for cue-word valence. ...........................................................................42
3-4 Subjective ratings for cue-word arousal. ...........................................................................43
3-5 Dot-probe task reaction times for no shock (ICD) and shocked (ICD) patients. ...............43
3-6 Dot-probe task error rates for no shock (ICD) and shocked (ICD) patients. .....................43
3-7 Average attentional bias scores (incongruent-congruent). Negative scores indicate decreased bias. ...................................................................................................................44
9
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ATTENTIONAL BIAS IN PATIENTS WITH IMPLANTABLE CARDIOVERTER DEFIBRILLATORS: EXAMINING MECHANISMS OF HYPERVIGILENCE AND
ANXIETY
By
Neha K. Dixit
August 2008
Chair: William M. Perlstein Cochair: Samuel F. Sears Major: Psychology
Symptoms of anxiety and hypervigilance are prevalent in patients with arrhythmias,
particularly in patients with life threatening arrhythmias such as ventricular fibrillation (VF).
The treatment of choice for patients with VF is implantation of an implantable cardioverter
defibrillator (ICD) which shocks patients out of life threatening arrhythmias and places them at
risk for shock specific anxiety secondary to living with their device. Literature examining
affective influences on attentional processing suggests that people with high levels of anxiety
have biased attention towards threatening information, such that they have difficulty disengaging
attention from negative or threatening stimuli. Using a modified emotional dot-probe task, we
examined attentional bias in patients with ICDs comparing them to patients with atrial
fibrillation (AF). Contrary to predictions, ICD patients did exhibit attentional bias towards
clinically relevant information compared to AF controls, and levels of state and trait anxiety did
not influence the magnitude of attentional bias in either group. ICD patients demonstrated higher
levels of trait anxiety compared to AF patients as well as worse physical functioning.
Additionally, results demonstrate efficacy of affective stimuli, with ICD patients rating clinical
words as more unpleasant than AF controls. Overall, results suggest that this paradigm must be
10
examined and potentially modified in greater detail to elucidate the influence of affective cue
words on attentional bias in the arrhythmia population.
11
CHAPTER 1 INTRODUCTION
Overview and Study Aims
Each year, approximately 350,000 Americans experience sudden cardiac death (SCD)
related to the occurrence of cardiac arrhythmias, including ventricular fibrillation (VF) and
ventricular tachycardia (VT; American Heart Association, 2004). The Implantable cardioverter
defibrillator (ICD) is the treatment of choice for ventricular cardiac arrhythmias (Anti-
arrhythmic versus Implantable Device [AVID] Investigators, 1997; Moss et al., for the
Multicenter Automatic Defibrillator Implantation Trial [MADIT] Investigators, 1996), and
nearly 60,000 Americans receive an ICD each year. Although the ICD has demonstrated
impressive mortality benefits, the device nonetheless presents as a potential instigator of
psychological maladjustment in recipients. This is primarily due to the shock mechanism
necessary for the device to convert potentially lethal arrhythmias. Significant rates of panic
symptoms (Godemann, Butter, Lampe, Linden, Werner, & Behrens, 2004) and avoidance
behaviors (Lemon, Edelman, & Kirkness, 2004) have been documented among this population,
as have difficulties with depression, anxiety, interpersonal functioning, and stress management
(Sears & Conti, 2003).
Researchers have also implicated anxiety about the device and health related anxiety as
significant predictors of psychosocial distress (Pauli et. al, 1999; Sears, 1999). Given the high
levels of susceptibility for both device related and generalized disease specific anxiety in ICD
recipients, it is critical to identify areas of cognitive functioning which may be affected by such
distress and may also serve to maintain or exacerbate such distress. Considerable research has
shown that selective attention, the ability to attend to and ignore information in the environment,
may be a critical cognitive process that is affected by both normal and clinical (i.e., social and
12
specific phobic) anxiety (Compton, 2003). Emotional processing is tightly linked to levels of
arousal (Damasio, 1996), such that high levels of arousal (e.g. fear, threat) may enhance attention
during a threatening situation and low levels of arousal may allow an individual to ignore
relevant information. Patients with cardiac disease, specifically arrhythmias, constantly evaluate
their own levels of related threat (Pauli, 1999). The nature of their disease state warrants critical
vigilance to symptomatology, adherence to medication regimens, knowledge of health care
options and advances, and a host of medical information which may vary throughout the course
of living with chronic cardiac disease. Attention or vigilance to medical knowledge and
personal health status is important as it keeps arrhythmic patients focused on returning to full
functioning. Too much or too little attention to such information can result in diminished
physical and mental functioning. Thus, examining the levels of attention to cardiac specific
information in ICD recipients (for whom anxiety is directly related to physical symptomatology
and device specific characteristics) may give rise to further characterizing and understanding
patients’ beliefs and specific fears about their devices and disease state for future interpretation.
Specific Aims
The current study aimed to examine cardiac-specific attentional biases in patients living
with ICDs. Patients with ICDs provide a unique perspective on the relationship among emotion,
attention, and anxiety given the nature of the acquisition of their symptoms. ICD recipients who
are psychologically healthy prior to implantation may experience clinical levels of anxiety
following ICD implantation and the experience of ICD shock. Examining ICD patients’ ability
to disengage attention from shock-related information is critical to their quality of life and
psychosocial health. It is important for patients to redirect from negative material (e.g. counting
their pulse, catastrophic thoughts) in order for them to retain information provided by physicians
and live actively with the benefits of the device and minimize drawback. Previous research has
13
suggested that a variety of anxiety-disordered patients demonstrate attentional biases towards
clinically-relevant information (Derryberry & Reed, 1994) and that attention to threatening
information is directly related to coping style and functional strategy. Research has
demonstrated that patients with adaptive coping strategies are able to disengage better from
negative disease specific information than those with maladaptive strategies. Thus, examination
of attentional bias for cardiac-related information in ICD recipients could prove useful in
tailoring future treatments to these patients.
• Specific Aim 1: To determine if the implantation of an ICD in cardiac patients, the frequency of ICD shock, and generalized state or trait anxiety are associated with specific affect-mediated selective attentional biases as measured using a variation of the Dot Probe task. Based on the literature suggesting attentional bias towards threatening information in individuals with high levels of anxiety (i.e. state) and specific phobias, it is hypothesized that both levels of distress and the presence of at least one ICD shock will contribute to biased performance, reflected by disproportionately slower reaction times under clinically-relevant cued conditions compared to both arrhythmia control patients and non-ICD-related cues.
• Specific Aim 2: To examine if varying levels of state and trait anxiety differentially affect attentional bias in both ICD patients and arrhythmia controls. Evidence suggests that living with an arrhythmia (whether life threatening or not) may increase the extent of ones’ bodily or cardiac-specific vigilance. Additionally, studies of attentional bias in participants with sub-clinical levels of anxiety also show a bias towards threat-relevant information. It is hypothesized that the magnitude of attentional bias to threat will be positively correlated with levels of state, but not trait, anxiety in both groups as demonstrated in previous studies examining attentional biases in otherwise healthy individuals (Fox, Dutton, & Bowles, 2001). State anxiety has been implicated in attentional bias in anxiety-disordered individuals, whereas trait anxiety has not generally been correlated with indices of bias in similar dot probe paradigms (Mogg & Bradley, 2002).
In sum, the proposed research will examine potential selective-attention biases to cardiac-
related information in patients who have received ICDs. The significance of this research is two-
fold: 1) it may enhance our understanding of attentional biases in patients who are potentially
developing heightening anxiety and concern with bodily symptoms, thereby providing a
prospective means by which to study natural anxiety-disorder development, and 2) it may
provide insight into treatment of cardiac specific anxiety-related symptoms in patients with ICDs
14
which can potentially improve their quality of life, lead to better adherence to treatment
regimens, and improved understanding of their disease process.
Background and Significance
The following sections will first describe the prevalence and presentation of psychosocial
distress in patients with ICDs and arrhythmias. Then, the use of the dot-probe paradigm to
examine attentional bias in a variety of anxiety-disordered individuals will be discussed. Finally,
the relationship between ICD placement, shock, anxiety, and attentional bias will be explained,
including possible mechanisms for the observed relationship.
Sudden cardiac death is an increasingly frequent occurrence among patients with
cardiovascular disease, particularly those with conditions compromising the electrophysiology of
the heart. Recent advances in device technology have increased delivery of preferred treatment
of life-threatening arrhythmias. A patient is considered at risk for sudden death if they have had a
previous cardiac arrest from which they have been resuscitated, if they have an ejection fraction
<35%, if they have a history of congestive heart failure, or have congenital cardiac issues such as
long QT syndrome exist, where sudden death is a common outcome. ICDs are devices that
prevent the heart from either going into a life threatening rhythm or shock the heart back from a
chaotic rhythm. Sears and colleagues (1999, 2000, 2001, 2002, 2003, 2004), as well as other
researchers, have discussed aspects of psychosocial distress related to living with an ICD. The
two domains which have been commonly examined are affective and mood disturbances in
patients and quality of life changes in patients. Sears (2003) has reported that the prevalence of
anxiety symptoms in ICD patients is between 13-87% with rates of clinically-significant anxiety
ranging between 15-38%. Rates of depression in this population are around 12-24%. Given these
numbers, and the findings of researchers such as Hegal et al, (1997) who report that 30% of all
recipients of ICDs have clinically-relevant depression and anxiety, psychosocial distress is an
15
important factor to examine in the life course of these patients. While anxiety and depression do
exist in this population, it is important to note that rates of depression appear to be similar to
those in the general cardiac population (Sears and Conti, 2003). It is the rates of anxiety and the
unique development of this anxiety which distinguishes ICD patients from other medical
populations (Godeman, 2004).
One of the interesting issues surrounding psychosocial distress in ICD patients is in the
way one can attribute the distress. The CABG-PATCH trial, examined the quality of life and
psychosocial distress in recipients of ICDs versus those who did not receive ICD post bypass
surgery. Researchers in this study noted significant distress among the ICD group compared to
the non-ICD group. Examining these data more thoroughly revealed that it was patients who
received shocks who perceived their quality of life as diminished and contributed to the distress
ratings in the group. Several other researchers have also implicated shock as an important
contributor to psychosocial distress. Schron et al. (2004) showed that patients who got shocked
in the first 6 months of receiving their device had a greater incidence of depression and anxiety
than their non-shocked counterparts. Several other factors have been implicated in poor
psychosocial functioning in recipients of ICDs including age, gender, premorbid psychological
functioning, and general life coping skills (Sears et al, 1999). Pauli and colleagues (1999) have
shown that individuals who adopt a coping style involving catastrophizing have more
psychosocial distress and are less able to cope with both their device but also the aspects of
having a life threatening condition. Such types of distress may manifest themselves in the
inability to adequately manage treatment regimens, and intake of vital medical information. The
accuracy of disease perception is critical to quality of life and survival of ICD patients.
16
Psychosocial Effects of ICD Implantation
Patients with life threatening arrhythmias face numerous medical and psychosocial
challenges in today’s environment. As stated previously, the advent of technology allows
patients to live longer and more resilient medical lives, but in many patients the ICD comes at a
price to their quality of life and mental health. Specifically, psychosocial and quality of life
issues that coincide with implantation are being more carefully dissected.
Anxiety and the ICD Patient
Anxiety has been identified as a significant contributor to the pathogenesis of cardiac
disease (Kubzansky, Kawachi, Weiss, & Sparrow, 1998). Through activation of the sympathetic
nervous system and subsequent release of catecholamines, anxiety is implicated in platelet
aggregation, injury of arterial lining, and release of fatty acids into the blood – all of which
promote the atherosclerotic process. Anxiety also may cause injury by decreasing heart rate
variability and increasing the incidence of ventricular premature beats, thereby contributing to
electrical instability. Finally, anxiety may trigger a myocardial infarction (heart attack) due to the
association between hyperventilation and coronary vasospasms. Behavioral mechanisms have
also been established associating anxiety with health-compromising activities, such as smoking,
decreased physical activity, or poor diet (Haywood, 1995; Januzzi, Stern, Pasternak, &
DeSanctis, 2000).
Anxiety is the most common complaint among ICD patients who have been shocked. A
number of studies have shown that recipients of ICDs experience psychological distress as a
result of receiving one or multiple shocks. The role of classical conditioning in the presence of a
predominantly neutral stimulus (non-shock ICD placement) plays an important role in the
development of anxiety and psychological symptoms (Sears et al, 1999; Godemann, 2004).
When an arrhythmia occurs, the patient receives a high-voltage shock to the chest. This is
17
intuitively an anxiety-provoking and fearful experience for patients (Herrman, von zur Muhen,
Schaumann, Buss, Kemper, Wantzen, & Gonska, 1997; Luderitz, Jung, Deister, & Manz, 1996;
Schuster, Phillips, Dillon, & Tomich, 1998; Sears, Todaro, Saia-Lewis, Sotile, & Conti, 1999).
Research indicates that excessive cardiac worry, ICD-specific fears, as well as physiological
arousal are among the anxiety symptoms experienced by patients with ICDs (Sears et al., 2000).
Research has shown that up to 15.9% of patients who receive an ICD develop one or more
anxiety disorders (e.g., panic disorder, generalized anxiety disorder) after implantation
(Godemann et al, 2004). Accordingly, as many as 40% of ICD patients may exhibit clinically
significant symptoms of anxiety (Sears & Conti, 2002).
Arrhythmias and Hypervigilance
Living with arrhythmias of any kind can be as stressful as living with other chronic
illness. What makes arrhythmias even more difficult to live with is the specific nature of the
symptoms they produce. A patient with atrial arrhythmia, for example, may feel fluttering of the
heart, tightness in the chest, and dizziness, among other symptoms. They may also feel nothing.
However, the consequence of their particular arrhythmia may be life threatening. Most patients
with atrial fibrillation (a common arrhythmia in the elderly population) are on anti-coagulant
therapy due to the high rates of thrombolic strokes which occur in these patients secondary to
their arrhythmia. In the same regard, a patient with diagnosed susceptibility to supra ventricular
tachycardia (SVT), ventricular tachycardia (VT), or ventricular fibrillation (VF) may feel dizzy,
faint, have difficulty breathing and feel like their heart is racing. If these patients have ICDs, in
most cases the response to their arrhythmia will be shock. Not surprisingly, it is not only the
seriousness of the condition itself in patients with arrhythmias that contributes to anxiety and
psychosocial distress, but the nature of the symptoms which are often themselves anxiety
provoking (Burke, 2004). For example, Godemann (2004) found that ICD patients who had been
18
shocked were three times more likely to have diagnosable panic disorder and generalized anxiety
disorder than their non-shocked counterparts. Evidence from the ICD literature (Pauli, 1999,
Sears and Conti, 2003) shows that patients are constantly evaluating their level of health.
Negative cognitions such as “will I die from this shock?” or “will by heart stop beating?” are
acceptable and real questions for patients to ask themselves. The nature of their illness requires
them to question their bodies. Problems arise when patients go from healthy questioning of
symptoms to an unhealthy hypervigilence of their bodies. These individuals focus so much on
factors determining their health status (e.g. checking their pulse, counting respirations, trying to
predict shock), that they forgo living and general quality of life. Mallioux and Brenner (2002)
described this phenomenon as “somatosensory amplification” in which patients overemphasize
the responses of their parasympathetic nervous systems to normal stimuli and then worry about
their health after making an inaccurate attribution. The illusion of control which is maintained
by patients who are exceptionally anxious or hypervigilent is dangerous because it is not real. It
is not based on actual physical merit and can actually cause increased numbers of arrhythmias,
thus perpetuating the cycle of vigilance and distress.
Anxiety as Precipitant to Shock
Recent research in the ICD literature points to growing evidence that stress and anxiety
are themselves contributers to shock. A study by Shedd and colleagues (2004) examined the
incidence of shocks 30 days before the attacks on world trade center and 30 days post. Findings
demonstrated a 2.8 fold increase in shock after the WTC attacks among people living in Florida,
while researchers examining individuals in New York City and other parts of New York found
similar results at a 2.4 fold increase. Both studies controlled for other mitigating factors which
may have contributed to shocks. These numbers indicate that traumatic life events even far from
their occurrence increase stress and cause arrhythmias to occur. Dunbar (1999) also showed a
19
relationship between aggression, hostility and shocks leading to the suggestion that there are
patient relevant personality/trait factors which contribute to shock. Neurochemical support of
these findings has been demonstrated by Lampert and colleagues (2004) who showed that higher
levels of stress hormones (epinephrine and norephinephrine) were correlated with arrhythmic
changes in the heart suggesting a biochemical pathway which may be excited when a patient gets
anxious or stressed. Collectively, greater insights into anxiety processes may allow for some
impact on the occurrence of shock itself.
Selective Attention
Selective attention is an important component of a human’s cognitive experience. The
brain’s ability to make decisions about what information to attend to and what information to
filter out is vital to maneuvering through the vast array of environmental and internal stimuli we
perceive and take in. Several researchers have examined different models of attention. Posner’s
model of attention is well known to decompose the components of selective attention and aid in
the understanding of mechanisms that comprise this system. This model of attention views
attention as a system comprised of several voluntary and involuntary processes (Posner &
Peterson, 1990; Posner & Raichle, 1994), which act in concert to orient a person to their
environment. Selective attention is driven by the posterior attentional system that is defined as
the “reactive” component of attention that orients a persons’ focus from one location to another.
According to Posner, orienting is accomplished through three operations: disengagement from
the object, movement to another location, and engagement of that new location. This model
theorizes that visual spatial attention involves both facilitation and inhibition of various
competing visual information. In his seminal exogenous cueing task, Posner (1988) found that
presentation of a visual cue increases a subject’s vigilance and orients them to that spatial
location, thus allowing for faster target detection in that location. While a subject orients to the
20
new location, he/she inhibits all other spatial information. Classical paradigms used to examine
selective attention and orienting, have utilized Posner’s exogenous cueing paradigm and his
theoretical principles in the experimental setting.
Attentional Bias and Emotion
Affective influences on information processing is critical for human function. The
adaptive function of emotion depends upon the particular emotion being studied but basic
emotions such as anger, fear, happiness, sadness, and disgust evolved distinctly to benefit the
human experience (LeDoux, 1996; Lang, Davis, & Ohman, 2000). For example, it is likely that
the basic emotion of fear evolved to enable an organism to rapidly detect and respond to danger
in its environment (LeDoux, 1996). Contemporary theories of emotion argue that the initial
appraisal of a situation or object (as neutral, positive, or negative) is one of the major
determinants of the emotional response to that situation (Lazarus, 1966; Oatley & Johnson-Laird,
1987). Since emotional appraisal of an external stimulus may also determine its importance or
priority, attentional input to that stimulus may be guided by such an appraisal (Lang, Bradley, &
Cuthbert 1997; Damasio, 1998; Compton, 2003). Thus, given the vast amount of information in
our external environment it is adaptive for emotional processing of stimuli and attentional
selection to be integrally related.
Of particular interest to researchers who examine the interplay between emotion and
attention is the speed with which appraisals and attentional shifts are made. For example, several
researchers have demonstrated that emotional processing is encoded early in the processing
stream and is fairly “automatic” (Ohman, 1997, Zajonc, 2000). Automatic processing has been
defined by a time frame between 100-300 milliseconds after the appearance of an emotional
stimulus (Compton, 2003). Neuroimaging techniques have allowed researchers to examine brain
activity during these early stages of processing. One such technique is event-related potentials
21
(ERPs) which record fast electrical changes in the scalp during stimulus presentation. Studies
using ERPs have demonstrated that discrimination of emotional content (e.g. face recognition-
happy, sad, angry; provocative pictures) occurs during as early as 80-160 milliseconds with the
onset of the stimulus (Broomfield & Turpin (2005). Masking studies, where the emotional
stimulus is imperceptible to conscious processing (Lang, Davis, & Ohman, 2000), have also
demonstrated early detection of both the content of emotional stimuli (pleasant, neutral, or
unpleasant) and intensity of the emotional connotation (arousal).
Functional neuroimaging has provided additional insight into the relationship between
selective attention and emotion. Neural structures involved in the early processing of emotional
stimuli include the amygdala, anterior cingulate, and frontal cortex (Dolan, 2000). There is a
large degree of overlap between these structures and those involved in processing selective
attention. Two main neural mechanisms exist by which emotion may guide information
processing. Regions of the brain that rely on sensory information such as the visual cortex and
the extrastriate cortex regulate bottom-up influences on attention. During presentation of an
emotional stimulus these regions show increased activity or amplification resulting in favored
attentional selection to that stimulus (Mangun, Jha, Hopfinger, & Handy, 2000). Amplification
is thought to occur via bottom up input from the amygdala that reacts to emotional representation
put forth. For example, Lane and colleagues (1997, 1998) found that exposing subjects to
emotionally- arousing pictures increased activation in the visual cortex compared to neutral
pictures. Others such as Pessosa and Ungerleider (2004) have found increased activation of
areas such as the fusiform gyrus (also involved in visual processing) when showing subjects
fearful verses neutral faces.
22
While the amygdala sends amplifying signals to sensory cortices, other regions of the
brain are implicated in top-down processing of emotional information to help modulate its
selection. The two major brain regions involved in selection and suppression of information
from the amygdala are the dorsolateral and ventromedial corticies (Mangun et al, 2000). The
dorsolateral region is involved in selecting and maintaining stimulus attributes in working
memory (Cohen et al, 1999, 2000), while the ventromedical region is involved in registering the
emotional significance of stimuli and is also involved in motivational and goal directed
processing (Bush, Luu, Posner, 2000). The anterior cingulate which is a part of the ventromedial
frontal cortex, is a structure involved in conflict detection and may play a role in what emotional
information to let into decision making processing and which to leave out (Hariri et al, 2004;
Whalen & Bush, 1998). Bishop and colleagues (2004) showed using an fMRI study that,
individuals who were highly anxious had higher anterior cingulate cortex (ACC) activation
compared to less anxious comparison subject when viewing threatening stimuli. According to
the somatic marker hypothesis of Damasio (1994) feedback from autonomatic (emotional)
responses provides critical input via the amygdala to decision-making processes mediated by the
frontal lobes. Given the neural mechanisms involved in emotional processing, theories of normal
emotion have their parallel in theories of disordered emotions such as anxiety and depression
where thinking, cognitive processing, and decision making has been shown to be distorted (Beck,
1976).
Experimental Paradigms Examining Attentional Bias
Two common experimental methodologies used to examine selective attention in adults
are the emotional stroop task and the emotional dot probe paradigm. In both tasks, the basic
premise is to orient a subject’s attention to particular stimuli while utilizing interfering emotional
stimuli to distract the subject. Together, these two tasks have been manipulated such that
23
researchers have been able to determine the nature of both cognitive and neuroanatomical
aspects of anxiety disordered individuals and their ability/inability to attend meaningfully to
specific stimuli.
Since its inception the Stroop has commonly been used as the gold standard for selective
attention tasks. In this particular task, a subject is asked to read a set of emotional and neutral
words and then asked to name the color of the word disregarding the word’s content. It has been
demonstrated by numerous researchers that subjects’ response latencies to emotional words are
longer for subjects for whom the words have relevance (e.g. socially relevant words for social
phobias) compared to control subjects. The results indicate that the automaticity of word naming
is overridden by the emotional content of the word. In fact word naming appears to take longer
when examining emotional words for people with affective disorders. It has been posited that
the Stroop is a task of conflict detection and monitoring. Although attentional networks have
been implicated in this detection process, the results of the emotional version of this task are
often misguided and interpreted inaccurately (Algom, Chajut & Lev, 2004). In the Stroop it is
often the level of emotional content within the word that drives the attentional bias and is not
considered a hallmark stroop color naming effect. The emotional content of the word itself is the
interfering stimulus and can vary on its level of biasing attention. The dot probe task, a derivative
of Posner’s original exogenous cuing paradigm, is more commonly used to examine attentional
bias in healthy and mood disordered individuals (Posner, 2000).
The dot probe paradigm orients a person’s attention to a particular spatial location by the
presentation of a cue prior to a target probe. In this task individuals are asked to attend to letters
or dots presented in different spatial locations on a computer screen (cue). They are then shown a
target probe in the same or different location of the previously presented cue and asked to
24
respond to the probe. The basic premise of the dot probe paradigm is that a person’s visual
selective attention can be oriented differentially to spatial locations. A subject’s reaction time
measured by response latencies between cue and target (i.e., probe) detection is the main
measure of their attentional capture. Further analysis of the dot probe task involves examination
of response latencies subjects have to valid trials and invalid trials, sometimes called the validity
effect. Longer response latencies are observed for trials where probes occur in a different
location from the cue (i.e., invalid or incongruent) suggesting that individuals are primed by the
cue to orient their attention in one direction and have difficulty disengaging from that location in
response to the probe. The dot probe paradigm has utilized the principles of Posner’s “shift” and
“disengage” components of attention (1980) to describe instances of disturbed selective attention
during the task. The task has been manipulated in numerous ways to examine selective attention
in anxious individuals mainly with the addition of emotional cue related stimuli; usually an
emotional word, face, or picture and by priming locations in a valid/invalid manner to create an
attentional response bias.
Evidence of Attentional Bias in Anxiety Disorders
Individuals with anxiety disorders are of particular interest when examining attentional
biases because of the nature of the disease state. Mood congruent attentional biases are well
established in the anxiety literature (Williams, Watts, MacLeod, &Mathews, 1997; Armony
&LeDoux, 2000). It has been posited that human anxiety reflects a heightened response of the
fear system (Lang & Ohman, 2000; Armony & LeDoux, 2000; Fox, Russo, Bowles, & Dutton
2001). Thus, it is adaptive for people who perceive a threat to get anxious and thereby engage
neural systems to aid in the resolution of the threat. If resolution cannot be reached, higher order
brain systems (frontal cortex, etc.) must come online and create alternative response options.
25
As such, both individuals with anxiety disorders and those with subclinical levels of anxiety may
differentially strategize execution of action during the presence of threat (Derryberry & Reed,
2004).
Major findings in the dot probe literature demonstrate that anxious individuals show a
bias towards threatening faces, words, and negative pictures. A study by MacLeod, Mathews
and Tata (1986) demonstrated, using an emotional dot probe task, that anxious patients were
faster to respond to the probe (dot) when it appeared in the location where a threat-related word
has just appeared (valid cue) compared a non-threat related word. This effect was
disproportionately seen in anxious individuals compared to non-anxious control participants and
was specific to threat-relevant information. This result has been replicated throughout the
literature (see Mogg & Bradley, 2000 for review) and suggests that threatening information
captures visual attention particularly in those individuals who are especially sensitive to fear-
relevant stimuli in the environment (e.g. anxious individuals). Hypervigligence to external
stimuli and processes of relevance such may divert attentional resources away from non-threat
related information and bias attention towards threat related information. Similar findings have
also been shown in non-clinical populations (Fox et al, 2001). Individuals with subclinical levels
of anxiety (high trait anxiety) have also demonstrated an attentional bias towards threatening
information (Fox et. al, 2001, Wilson & McLeod, 2003) when compared to low trait- anxious
individuals.
An important issue raised in the dot probe literature is one of individual differences in
anxiety-disordered patients. While some patients may exhibit heightened attentional capture to
threatening information, it has been demonstrated that some have difficulty disengaging from
threatening stimuli. A series of studies have shown individuals with high levels of state anxiety
26
to have difficulty disengaging from negative or threatening information (Amir, Elias, Klumpp, &
Przeworski, 2003; Fox et al, 2001; Yiend & Mathews 2001) that is particularly relevant to them.
Personal or individual threat therefore is an important consideration when interpreting dot probe
findings.
Other models discussing biased attentional direction (e.g. Williams, MacLeod) posit that
high trait-anxious individuals orient their attention towards threatening information while low
trait anxious individuals will orient away from the threatening information. The shifted
attentional model account proposed by Mogg and Bradley (2000), posits that regardless of level
of anxiety, all individuals will direct attention away from mild threat intensity stimuli and orient
towards stimuli with a high threat intensity. The observable difference between the two groups
is the intermediate levels of threat intensity. McLeod and Wilson (2003) designed a unique
study in which they varied the intensity level of a variety of angry faces on a continuum of
threat. Findings showed that all subjects showed greater vigilance (longer response times) to the
most extreme and intense faces. They did not show any effects at very low levels of intensity.
The critical difference in this study was to intermediate levels of angry faces. High trait anxious
individuals displayed a greater vigilance to threat compared to low anxious individuals. These
findings appear to suggest another mechanistic view of anxious individuals. They appear to
predict that high trait anxious participants reach a threshold of subjective threat at lower levels of
perceived threat than low anxious individuals.
The ability to disengage from personally relevant threat has been demonstrated in
medical populations. Researchers have demonstrated that patients with chronic pain show
attentional bias towards pain related information (e.g. words) when compared to medical
counterparts who did not have chronic pain (Dehgani, Sharpe, & Nicholas, 2003; Beck et al,
27
2001). In fact, pain patients show differential bias towards words that are related to their
particular type of pain. For example, Van Damme, Lorenz, Eccleston, Koster, DeClercq, &
Crombez, 2004 showed that patients with increased negative cognitions about their pain were
more likely to have increased response latencies to affective pain words compared to sensory
pain words. Patients rating their subjective pain experience as more intense (e.g., burning,
stinging) showed increased response latencies to sensory words compared to affective words.
Similar findings, demonstrating disproportionately increased response latencies to clinically-
relevant words compared to other words and compared to controls exist in literature examining
social phobia, specific phobia, and generalized anxiety disorder (Compton, 2003).
In sum, the dot probe literature highlights what has been interpreted as the highly anxious
individual’s inability to disengage from relevant threat information as evidenced by longer
response latencies during negative verses pleasant conditions. This generally results in
disproportionate slowing during clinically relevant or threatening conditions during invalid
verses valid trials.
Significance
The present study furthered an understanding of the relationship between anxiety and
cardiac arrhythmias, specifically the role of information processing in ICD recipients. The
number and proportion of individuals being implanted with ICDs is growing in this country.
With new advances in technology, ICDs will increase in their favorability as treatment of choice
in both arrhythmias and congestive heart failure. Research suggests the presence of anxiety,
specifically shock-related anxiety results in an increase in hypervigilence to bodily symptoms
and health-related stress (Pauli et. al, 1999; Sears et al, 2001; Godemann, 2004). In addition,
shock-related anxiety is associated with depression and decreased quality of life (Sears et al.,
1999, 2001, 2003). ICD recipients are, by nature of the mechanism of their device and disease
28
state biased towards potentially threatening sensations from their bodies. Much like studies of
attentional bias in pain patients, ICD recipients offer a unique perspective to examine the
relationship between emotion and attentional bias. In addition, the present study has clinical
significance in that findings may identify mechanisms by which ICD recipients may process
information, particularly cardiac related information, and to what degree they may over engage
this information is critical to treating them. This line of research may aid in the development of
individually tailored psychosocial interventions and the types of patient information that is
offered in a clinical setting.
29
CHAPTER 2 EXPERIMENTAL DESIGN AND METHODS
Participants
Thirty seven VF (ICD) patients (ages 34-80) and 41 AF patients (ages 37-80) participated
in the study. Participants were recruited through the Electrophysiology Clinics at Shands
Hospital at the University of Florida Health Science Center. Per interview, all participants were
right-handed native-English speakers. Our sample consisted of 93% Caucasian, 4% African-
American, and 3% Hispanic. Potential participants were excluded from the study for the
following reasons: 1) Major Axis I psychopathology; 2) dementia or other neurological disease;
3) acute medical illness; 4) current use of antiepileptics or other medication known to
significantly affect cognitive functioning; 5) motor deficits that would interfere with the use of
the dominant hand for performance of button press associated with the dot-probe task; and
6) a score of less than 30 on the Telephone Interview for Cognitive Status (TICS; Brandt,
Spencer, & Folstein, 1988). All participants provided written informed consent according to
procedures established by the University of Florida Health Science Center Institutional Review
Board. Participants were compensated $10 for their time.
Demographic characteristics of study participants are provided in Table 2-1. ICD and AF
patients were well matched for education, t(78) = -.57, p > .68, and were screened for reading
using the North American Adult Reading Test (NAART; Blair & Spreen, 1989; Nelson, 1982).
ICD patients and AF patients reported similar levels of depressive symptoms on the Beck
Depression Inventory, 2nd Edition (BDI-II; Beck, 1996), t(78) = -.03, p > .90. ICD patients and
AF patients also reported similar levels of state anxiety state, but greater trait anxiety, compared
to AF patients t(78) = 1.29, p < .04.
30
Medical data on cardiac diagnoses, current medication, and ICD-related information was
also obtained for purposes of characterizing the two groups. Mean ejection fraction was 35.87
(S.D. = 14.36). Respondents’ medical history was significant for ventricular tachycardia (21%),
ventricular fibrillation (11%), coronary artery disease (45%), and myocardial infarction (23%).
Seventy two percent of the sample had been diagnosed with congestive heart failure. Medication
use was as follows: 58% endorsed taking aspirin, 51% Coumadin, 84% beta-blockers, 15%
calcium channel blockers, 30% ACE inhibitors, 20% angiotensin receptor blockers, 48%
diuretics, 10% amiodarone, and 5% sotalol.
Procedure
Participants attended one 1- ½ hour testing session. Prior to the first session, participants
were administered the TICS (Brandt et al., 1988) as an initial screen for cognitive impairment.
Potential participants with TICS scores of less than 30 were excluded from the study. Using this
cutoff score, the TICS has a reported sensitivity of 94% and a specificity of 100% for
distinguishing demented individuals from cognitively intact individuals (Brandt et al., 1988).
Thus, the TICS provided a means to exclude demented individuals from the study. No
participants were excluded using this criterion during recruitment for this study.
During the experimental session, all participants received a screening1 of relevant
psychiatric and medical history. Participants were also screened for neurological insult that
might be an exclusionary criterion. They were asked whether they have difficulty reading the
newspaper to determine visual acuity problems that might interfere with performing the
computer task. The presence and severity of depressive symptoms were assessed via the BDI-II.
1 Participants were screened for psychiatric conditions via clinical interview and review of the medical record.
31
Participants were also given several psychosocial measures to examine general
psychological and emotional functioning. The measures given were: (1) The Florida Shock
Anxiety Scale (FSAS) was developed to assess the fear and anxiety that patients commonly have
regarding the ICD and its shocks. This 16-item measure examines the cognitive, behavioral,
emotional, and social impact of shock anxiety; (2) Spielberger State-Trait Anxiety Inventory
(STAI), a clinical measure of anxiety; (3) SF-12, a generalized measure of health related quality
of life; (4) The Left Ventricular Dysfunction Questionnaire (LVD-36) a cardiac specific quality
of life measure; (5) The Beck Depression Inventory, 2nd Edition (BDI-II) and (6) Telephone
Interview for Cognitive Status (TICS). These measures are described in detail below.
Shock Anxiety
The Florida Shock Anxiety Scale (FSAS): This scale was developed in the Cardiac
Psychology Lab at the University of Florida for a previous study to assess the fear and anxiety
that patients may have regarding the ICD and its shocks. This 16-item measure examines the
cognitive, behavioral, emotional and social impact of shock anxiety. Full psychometric
validation available (Kuhl, Dixit, Wallace, Sears, & Conti, 2005).
General Anxiety
State-Trait Anxiety Inventory (STAI): The STAI is a 40-item self-report questionnaire
designed to measure both state and trait anxiety (Speilberger, Gorsuch, Lushene, Vagg, &
Jacobs, 1983). Trait anxiety is defined as a relatively enduring personality characteristic, or
more specifically, as anxiety proneness. State Anxiety is defined by a short-lived anxiety,
usually induced by an event or circumstance. Both of these indices of anxiety will be examined
to differentiate the extent and level of anxiety.
32
General Health-Related Quality of Life
Short Form-12 (SF-12): This measure was developed to gauge mental and physical
functioning and can be separated into two components: physical component summary (PCS-12)
and mental component summary (MCS-12). All scores of the SF-12 are comparable and highly
correlated with scores from the SF-36, from which it was derived, (ranging from .63-.97; Ware et
al., 1995; Ware, Kosinski, & Keller, 1996). The SF-12 reproduced 90% of the variance in the
SF-36 PCS and MCS measures in the United States and on cross-validation in the MOS (Ware et
al., 1996).
The Left Ventricular Dysfunction Questionnaire (LVD-36): This cardiac-specific
measure was designed to assess the impact of left ventricular dysfunction on daily life and well-
being. Responses are dichotomous (true or false). True responses are summed, which are then
calculated as a percentage; higher scores indicate worse functioning (i.e., 0 = best possible
score). The measure demonstrated high internal consistency in a sample with chronic left
ventricular dysfunction (Kuder-Richardson coefficient = .95) (O’Leary & Jones, 2000).
Depression
Beck Depression Inventory-2nd Edition (BDI-II): The BDI-II is a 21-item self-report
instrument assessing the presence and severity of depression symptomatology over the preceding
two weeks (Beck et al., 1996). Its internal consistency ranges from .91 to .93, its one-week test-
retest reliability is .93 and moderate to high correlations with other measures of depressive
symptomatology supports its convergent validity. BDI-II has been widely used in cardiac
populations (Carney, Freedland, Sheline, & Weiss, 1997) and is the gold standard for assessing
depressive symptoms in health-related populations (JAMA, 2000).
33
Cognitive Screener
Telephone Interview for Cognitive Status TICS: The TICS is a brief test of cognitive
functioning developed. The TICS is similar to the Mini-Mental Status Exam (Folstein, Folstein,
& McHugh, 1975), but has a more comprehensive memory assessment, designed for identifying
dementia. Potential participants with TICS scores of less than 30 were excluded from the study.
Research has demonstrated that it is as reliable and valid as face-to-face administration. It has a
sensitivity of 94% and specificity of 100% for distinguishing normal controls and demented
individuals (Brandt et al., 1988) and sensitivity of 82% and specificity of 87% for distinguishing
normal controls and amnestic mild cognitively impaired older adults (Cook, Marsiske, &
McCoy, 2006).
Reading
The NAART (Blair & Spreen, 1989; Nelson, 1982) was used to estimate overall reading
abilities.
Experimental Task
The computerized task was run on a DELL PC laptop computer using E-Prime software
for stimulus presentation and behavioral data collection. To ensure that participants understood
task instructions and to increase familiarity with the button-press procedure, participants were
pre-practiced on the computerized cognitive task. The task paradigm utilized was a modified
version of a classical dot-probe paradigm developed by Williams, Watts and McLeod (1988).
Figure 2-1 illustrates a sample trial of the dot-probe task used in this experiment.
The task comprised a briefly-presented word cue, shortly followed by a target to which
participants made a speeded button-press response. Specifically, participants were instructed to
focus on the center of the screen where they saw a fixation point. Each trial of the task began
with a centrally-located 200-ms duration fixation point followed by a cue word presented to the
34
top or bottom of the fixation cross. After 800 ms, the cue word was immediately replaced by a
dot “*” target, which appeared randomly in the same (congruent) or opposing (incongruent)
location as the word. Participants were instructed to respond to the presentation of the target by
pressing the “h” or “j” keys indicating the location of the dot as quickly and accurately as
possible. The dot serving as a target disappeared after the key press or after 4000ms. The inter-
trial interval from the target offset to the next fixation cross was 1200ms. The participants’
response time (with ms accuracy; RTs) and accuracy to the target were recorded as dependent
variables.
Participants performed a total 240 experimental trials, equally and randomly distributed
across four word types and two word positions. Each word was repeated four times during the
entire task. Fifty percent of trials were congruent, drawing the attention of the participant to the
area where the word and asterisk appeared, while the remaining fifty percent of trials were
incongruent, drawing participants’ attention to the area opposite the one where the asterisk
appeared. Trials were randomized for each word category, with each category presented an
equal number of times across the task.
Four different word types were employed as cues, including cardiac-specific threat words
(e.g. shock, defibrillator, flutter), non-cardiac-specific threatening words (e.g., fearful, scared,
danger), pleasant words (e.g. delighted, confident, happy), and neutral words (e.g. tile, doorknob,
bland). Positive, threat and neutral words were chosen from norms of emotional words taken
from the Affective Norms for English Words (ANEW; Bradley & Lang, 1999) and matched for
frequency of usage in English, average word length, and grammatical structure. Cardiac specific
words were chosen from a group amassed and rated by clinicians at the Shands EP clinics and
graduate students in the Cardiac Psychology Lab. The cardiac-specific words were selected
35
based on rating for “high” valence and arousal. Valence and arousal ratings were measured
separately using a computerized administration of the Self-Assessment Manikin (SAM; Lang,
1980). Both dimensions of valence and arousal were rated on a 9-point Likert scale with 1=least
pleasant/arousing and 9=most pleasant/arousing. Forty words comprised the final set of stimuli
(Appendix A). As a manipulation check, participants performed valence and arousal ratings for
each word seen in the experiment, using the Self-Assessment Manikin (SAM; Lang, 1980) after
completion of the dot probe task.
36
Table 2-1. Mean (+standard deviation) demographic and psychological test data for VF and AF patients.
Min/Max VF Patients
(n=37) AF Patients (n=41)
t-value
Age (years) 34/80 62.36 (13.72) 63.12 (9.99) -.280 Education (years) 12/20 14.44 (2.41) 14.76 (2.35) -.572 TICS (raw score) 34/50 42.60 (4.45) 40.13 (3.23) .451 BDI (raw score) 0/11 6.85 (4.50) 5.34 (2.31) -.03 STAI-S (raw score) 20/62 32.48 (9.82) 34.25 (12.35) -.678 STAI-T (raw score) 24/56 37.77 (6.04) 34.05 (9.87) 1.29* LVD-36 (total score) 5.56/100 62.27 (25.99) 71.47 (27.65) -1.336 SF-12 {Physical} 17.06/57.66 34.94 (9.20) 40.58 (11.65) -2.321* SF-12 {Mental Health} 24.50/65.14 50.49 (10.41) 49.15 (10.58) .555 Note: TICS = Telephone Interview for Cognitive Status; BDI-II = Beck Depression Inventory; STAI-S = State Trait Anxiety Inventory state score; STAI-T = State Trait Anxiety Inventory trait score ; LVD-36 = Left Ventricular Dysfunction Questionnaire *Groups significantly different at p <.05.
Figure 2-1. Example of a typical incongruent, clinically relevant trial.
37
CHAPTER 3 DATA ANALYSIS AND RESULTS
Data Analysis
Dot Probe Task
Dependant measures for the dot probe will include reaction times and error rates for each
of the experimental conditions. For analyses involving RT, we employed median RTs (Ratcliff,
1993) for correct responses. For analyses involving error rates, data were arcsine transformed
(Neter, Wasserman, & Kutner, 1985) prior to all analyses. This transformation was used to
normalize the distribution of the error data, which is often skewed because the error rates are so
low proportionately. Median correct-trial reaction times (RTs) and arcsine errors were
calculated for each participant and experimental condition, and subjected to separate Group x 2-
Cue Validity (Congruent, Incongruent) x 4-Cue Valence (Pleasant, Neutral, General Threat,
Cardiac Threat) Analyses of Variance (ANOVAs). Group served as the between-subjects factor,
and cue congruency and cue valence served as within-subject factors. To correct for possible
violations of sphericity, a Hyundt-felt epsilon adjustment was calculated where appropriate and
adjusted p-values and unadjusted degrees of freedom are reported. Effect sizes for ANOVAS
were measured using Eta squared. The following hypotheses were addressed in the analyses:
Hypothesis 1: A main effect of congruency will be seen across groups (slower RTs and greater error rates to incongruent than congruent trials).
Hypothesis 2: ICD patients, compared to arrhythmia controls, will exhibit a specific and disproportionate RT slowing to incongruent- relative to congruent-cue trials specifically involving clinically relevant words.
Hypothesis 3: There will be a significant 3-way interaction, reflecting disproportionate slowing of ICD patients to clinically specific incongruent vs. congruent cues compared to other word types and to AF controls.
38
Reaction Time Data
Overall, there was a significant effect of congruency in the opposite direction than
predicted, F(1, 78) = 16.377, p < .001, η2 = .98, with longer RTs to the congruent than
incongruent condition. There was no significant effect of valence, F(3,228) = .713, p>.55, η2 =
.15, nor was there a Group x Valence interaction F(3,228) = .478, p>.67, η2 = .11. Finally, no
Group x Congruency x Valence interaction, was found as hypothesized for RTs F(3,228) =
1.857, p>0.14, η2 = .27 (Figure 3-1).
Error Data
A main effect of group was observed for error rates, F(1, 78) = 16.099, p < .001, η2 = .98,
with ICD patients making greater errors overall, than AF patients. Next, we examined the effects
of cue type (valence) on dot-probe task performance. There were no significant effects of
valence on error rates, F(3,228) = .684, p>0.55, η2 = .02, nor was there a Group x Valence
interaction F(3,228) = .865, p>0.45, η2 = .01. Finally, no significant Group x Congruency x
Valence interaction was found for error rates, F(3,78) = .781, p>0.50, η2 =.01 (Figure 3-2).
Cue Word Valence and Arousal Ratings
To determine if the words selected for the emotional manipulation in the dot probe task
resulted in differential valence and arousal ratings within and between groups, post-task SAM
assessment valence and arousal ratings were analyzed using separate ANOVAs; with group as a
between-subjects factor and word category (Pleasant, Neutral, Unpleasant, Clinical) as a within-
subject factor.
Results indicate that the words were, indeed, effective in producing the desired effects: (1)
Both arousal and valence ratings for the Pleasant, Neutral, and Unpleasant words were consistent
with expectations, (2) the two groups did not differ in ratings of these standard words, (3) but
VF patients rated the clinically-relevant words as significantly more unpleasant than AF controls.
39
Cue Word Valence Ratings
Analyses of valence ratings demonstrated a main effect of valence, F(3,110) = 734.58,
p<0.001, p < .001, η2 = .99. This main effect was qualified by a significant Group x Valence
interaction F(3,110) = 11.32, p<0.001, η2 = .92. Follow-up independent samples t-test revealed
that ICD patients rated the clinically-relevant words as significantly more unpleasant than AF
patients, t(76) = 3.771, p<0.001; yet they did not differ in their rating of the other word
categories (Figure 3-3).
Cue Word Arousal Ratings
Analyses of arousal ratings revealed a significant main effect of word category, F(3, 110)
= 274.29, p<0.001, η2 = .87. In general both groups rated pleasant, neutral and clinical words as
more arousing than neutral words. No group by valence interaction was seen for arousal F(3,
110) = 3.07, p>.08, η2 = .12 suggesting that ICD and AF patients found the words, equally
arousing despite a priori predictions (Figure 3-4).
Effect of Shock on Dot Probe Task
Shock and Reaction Time
To examine the unique effects of shock on task performance, ICD recipients were
grouped into “shock” and “no shock” categories. Fourteen ICD recipients received one or more
shocks and 21 recipients had no history of shock. There were no significant effects of presence
or absence of shock with respect to RTs, F(1,36) = 2.15, p>.53, η2 = .08. Additionally, no
significant effect of valence F(3,102) =.774, p>.49, η2 = .13 or Shock x Valence interaction was
found F(3,102) = 2.15, p>.09, η2 = .23. Finally, no Shock x Congruency x Valence interaction
was found as hypothesized for RTs, F(3,102) = 1.288, p>0.33, η2 = .07 (Figure 3-5).
Shock and Error Rates
There were no significant effects of presence or absence of shock on error rates,
40
F(1,36)= .57, p>.45, η2 = .08. Additionally, no significant effect of valence F(3,102) =.1.037,
p>.35, η2 = .02 or Shock x Valence interaction was found F(3,102) = .90, p>.96, η2 = .05.
Finally, no Shock x Congruency x Valence interaction was found for errors, F(3,102) = 1.04,
p>0.37, η2 = .02 (Figure 3-6).
To further examine the impact of ICD shock on psychosocial measures of state, trait, and
shock related anxiety, additional ANOVAs were run using only ICD shock as the group variable.
No significant differences between groups were found for state anxiety, F(1,36)= .87, p>.35, η2
=.12, trait anxiety, F(1,36)= .28, p>.59, η2 = .15, and shock anxiety (FSAS), F(1,36)= .68,
p>.41, η2 = .20.
General attentional bias scores were calculated to qualitatively examine trends in the data
by subtracting incongruent RT trials from congruent RT trials (Figure 3-7). While no significant
differences emerged between groups (as discussed above); there was a pattern of decreased bias
to unpleasant and clinically-relevant stimuli compared to pleasant and neutral stimuli.
To examine the hypothesis that magnitude of bias was positively correlated with both
ICD and AF patients’ anxiety levels, Pearson correlations were calculated between bias scores
and psychosocial measures of state, and trait anxiety. No significant relationships emerged
amongst bias scores and anxiety all ps > .05
Psychosocial Data
Psychosocial measures described in the methods were used to characterize the sample.
ICD patients and AF controls were similar in their levels of psychological distress, such that they
endorsed low levels of depression; similar levels of state anxiety, cardiac specific quality of life
and general mental health (Table 2-1). Notably, ICD patients demonstrated significantly more
trait anxiety than AF controls t(78) = 1.29, p < .04, as well as worse physical health than AF
41
controls t(78) = . -2.321, p < .03. Additionally, when examining ICD patients alone, female
recipients endorsed a greater level of shock related anxiety than male patients.
Pearson correlations were calculated amongst psychosocial measures of anxiety. As
expected, state anxiety was positively correlated with trait anxiety r(78) = .709, p < .01,
suggesting, that AF and ICD patients who experience greater levels of moment to moment
anxiety are more likely to be generally anxious individuals. Additionally, because this study
highlighted participants’ experience of living with an ICD, state anxiety was positively
correlated with shock related anxiety r(37) = .412, p < .01.
42
400500600700800900
1000
Pleasa
nt
Neutral
Unpleas
ant
Clinica
l
Pleasa
nt
Neutral
Unpleas
ant
Clinica
l
VF AF
Rea
ctio
n Ti
me
(mse
c)CongruentIncongruent
Figure 3-1. Dot-probe task reaction times for VF (ICD) and AF (control) patients.
00.010.020.030.040.050.060.070.080.09
Pleasa
nt
Neutra
l
Unpleas
ant
Clinica
l
Pleasa
nt
Neutral
Unpleas
ant
Clinica
l
VF AF
Prop
ortio
n of
Err
ors
CongruentIncongruent
Figure 3-2. Dot-probe task error rates for VF (ICD) and AF (control) patients.
123456789
Pleasant Neutral Unpleasant Clinical
Word Valence
Ple
asan
tnes
s Ra
ting
VFAF
Figure 3-3. Subjective ratings for cue-word valence.
43
123456789
Pleasant Neutral Unpleasant Clinical
Word Valence
Aro
usal
Rat
ing
VFAF
Figure 3-4. Subjective ratings for cue-word arousal.
500550600650700750800850900
Pleasa
nt
Neutral
Unpleas
ant
Clinica
l
Pleasa
nt
Neutral
Unpleas
ant
Clinica
l
No Shock Shock
Reac
tion
Tim
e (m
sec)
CongruentIncongruent
Figure 3-5. Dot-probe task reaction times for no shock (ICD) and shocked (ICD) patients.
00.020.040.060.080.1
0.120.14
Pleasa
nt
Neutra
l
Unplea
sant
Clinica
l
Pleasa
nt
Neutra
l
Unplea
sant
Clinica
l
No Shock Shock
Prop
ortio
n of
Err
or
CongruentIncongruent
Figure 3-6. Dot-probe task error rates for no shock (ICD) and shocked (ICD) patients.
44
-29
-28
-27
-26
-25
-24
Ple
asan
t
Neu
tral
Unp
leas
ant
Clin
ical
Atte
ntio
nal I
ndex
Sco
re (m
sec)
Valence
Figure 3-7. Average attentional bias scores (incongruent-congruent). Negative scores
indicate decreased bias.
45
CHAPTER 4 DISCUSSION
Our study is the first to examine the relationships between disease-related anxiety and
attentional processing in patients living with ICDs. While many studies have examined ICD and
AF patient functioning from a biomedical perspective, evaluating symptoms, quality of life
outcomes, and medical outcomes (Sears, 2004; Sears & Conti 2005; Godemann 2004.), this is
the first examination of potential attentional bias in this patient population. Four main findings
emerged from the research: First, both ICD and AF patients showed a significant attentional bias
towards congruent information, irrespective of emotional valence of the task word cue. Second
and unexpectedly, ICD patients committed more errors overall than AF patients. Third, ICD
patients demonstrated worse general physical functioning and greater trait anxiety as a group
compared to AF patients. Contrary to predictions, however, the presence or absence of ICD
shock did not distinguish this difference. Finally, ICD patients found the clinically-relevant cue
words more unpleasant than did AF patients.
Evidence of Attentional Bias
A primary aim of this study was to examine the ICD patients’ ability to disengage
attention from shock-related (cardiac specific) information using a modified dot-probe paradigm;
that is, to determine if they exhibit evidence of a specific attentional bias towards clinically-
relevant stimuli. The hypothesis was that ICD patients would be slower to respond to
incongruent clinical trials compared to AF patients. Results demonstrated that regardless of
group (ICD or AF control) or emotional valence of the words presented in the task, both groups
were slower to respond to a probe presented at a congruent than incongruent location, suggesting
a “reverse” congruency effect. This finding is novel and generally inconsistent with the
literature on dot probe and Stroop tasks which consistently demonstrate that subjects have longer
46
reaction times under incongruent than congruent conditions. The literature also demonstrates that
emotional valence of the cue (e.g., word, picture, face) differentially affects a person’s ability to
respond to corresponding probes such that incongruent trials with affectively arousing words,
give rise to disproportionately slower responses.
Although the finding that participants were slower to respond to a probe presented
directly after an emotional word is surprising, past research has previously suggested similar
inconsistencies, particularly in older populations. Fox and colleagues (2005) demonstrated that
older adults with high levels of state anxiety did not demonstrate a classical interference effect
on an emotional Stroop task. In fact, their findings demonstrated that older adults have a
tendency to disregard the threat content of task relevant information and perform similarly on
both congruent and incongruent trials. The findings from this study and another by Mather and
Carstensen (2003) also suggest that older adults with low levels of anxiety tend to avoid
attending to negative emotional material. One explanation of the findings in this study is that
age may have factored into the response style of participants. Our participants did not endorse
clinically significant levels of state anxiety and were older than most cohorts in the classical dot-
probe literature. As such, they may have disregarded the emotional content of the words during
the task, focusing more on the directions asking them to respond to the probe.
The “reverse” congruency effect in this task may be better understood through paradigms
in the literature examining attentional engagement. Posner and colleagues (e.g. Posner, Cohen &
Rafel, 1982) demonstrated the concept of attentional engagement vs. disengagement to spatial
location using a cued target paradigm. In this task, participants were instructed to focus on a
fixation point between two rectangles, wait for a cue (the brightening of one rectangle) and
respond to congruent or incongruent probes presented in one of the two rectangles. Participants
47
in Posner’s experiment were faster to respond to congruent than incongruent trials, suggesting an
effect of cue dependency.
In the paradigm used in this study, it is possible that cue word reading prior to a
congruent probe response resulted in facilitated attention to that spatial location such that
participants attended to the word and responded to the probe in an “automatic” manner without
paying conscious attention to the probe itself. Additionally, directing gaze towards a probe in
the opposite location of the cue word (incongruent probe) may have required heightened
disengagement of attentional resources (to make a correct probe response) such that participants
could not dwell on the emotional content of the word presented. Finally, the magnitude of
attentional bias in this study was in the opposite direction demonstrating decreased bias towards
clinically-relevant and unpleasant words compared to neutral and pleasant words. This suggests
that participants spent less time attending to the clinical words compared to the non-clinical
words. While the result was not statistically significant, it may provide further evidence for the
“reverse” congruency effect.
Cohort Effects on Task
Contrary to expectations there was no effect of valence on task performance. A potential
explanation for this may be that participants were experiencing heightened arousal during the
entire task and may have allocated all possible resources to performing that task correctly at the
expense of attending to the valence of the words. Participants may have been over aroused
throughout the task such that valence specific effects were washed out. Direct measurements of
autonomic arousal (i.e., skin conductance) (Lang et al. 2000) may have been useful in gaining
information about physiological arousal in participants.
Another notable finding in this study was that ICD patients exhibited greater levels of
trait anxiety and worse self-reported physical functioning compared to AF controls. It is
48
plausible that this increased generalized anxiety led ICD patients to make more errors in the dot-
probe task compared to AF controls. Further support for this hypothesis is provided by the fact
that there was no speed-accuracy trade off in either group. ICD patients’ greater trait anxiety
may also help to explain why no main effects of valence were found. Mogg and Bradley (1998)
proposed that anxiety is the locus of the individual differences in a person’s threat appraisal
mechanism. These researchers posit that high trait-anxious individuals appraise all levels of
threat as subjectively greater than low trait-anxious individuals. As such, ICD patients in this
sample may have reached a high level of “threat vigilance” by viewing personally-threatening
(clinical) words and subsequently appraised other valences as more unpleasant across the trials,
suggesting the presence of a “carryover effect.”
Differences in Methodology
Another consideration given the general results of this study is the methodology
employed to elicit evidence of emotional attentional bias. The traditional dot-probe task
developed by McLeod and colleagues (1997) presented word pairs such that spatial attention
during the “cue” phase of the task was captured across the screen with a different set of stimuli
each time (e.g. a neutral word always accompanied an emotional arousing word). On the
traditional task, trials can be examined to delineate preferences towards and away from threat
related and neutral word pairs. The modified dot-probe task in this study was developed based
on findings by Amir and colleagues (2004), who did not employ the use of word pairs when
examining attentional bias in social phobics. Thus, it is possible that the task employed in this
study may not have been robust enough to elicit an attentional bias at the magnitude of other dot-
probe tasks. Another methodological difference that potentially influenced the results in this
study is the lack of a probability manipulation (Amir et al., 2004, Fox et al., 2000). Other
49
researchers have used a disproportionately larger set of congruent trials verses incongruent trials,
priming participants’ response patterns and increasing the magnitude of bias observed.
Manipulation Check
Finally, participants’ ratings of the valence and arousal of the cue words, demonstrate a
positive manipulation check. The ratings showed a disproportionate affective response to
clinically-relevant stimuli in ICD patients, who rated the clinically-relevant (cardiac) words as
more unpleasant compared to AF controls. One explanation of this finding is that the personal
relevance of the clinical words was greater for the ICD patients than the AF patients. An
example of a clinical word used in the task is “shock.” This word has clinical meaning for both
groups. For ICD patients shock may refer to their device going off, and be a reminder to them
that they have a life threatening arrhythmia. For AF patients shock may refer to an external
defibrillation treatment that helps to control their symptoms, potentially alleviating them. While
the word is relevant to both groups, it has the potential to connote greater negativity for ICD
patients than AF patients.
The pattern of findings suggests that both groups were equally aroused by the word-cues
irrespective of valence. Both the valence and arousal findings are novel and the first to be
demonstrated in an arrhythmia population. Given that both ICD and AF patients found the
cardiac words highly arousing, it is possible that the words developed for this sample did not
discriminate ICD related threat from AF related threat. Alternatively, the findings may suggest
that recipients of ICDs and patients living with symptoms of AF are more similar with respect to
levels of clinical hypervigilence than they are different. This is further evidence that the sample
in this study may have been too affectively homogenous to clearly elucidate the specific affective
attentional bias hypotheses.
50
Limitations
The current study represented a first step in the application of principles and paradigms of
cognitive neuroscience to the study of attentional processing in ICD recipients. Additionally it
offered a unique perspective in merging mechanistic research with demonstrated psychosocial
phenomena. Like many studies examining novel populations and paradigms, it ventured into
uncharted territory and potential limitations must be addressed. A number of pragmatic and
resource constraints may have affected the results.
The first limitation of the present study may have been with the study sample itself. The
participants were highly selected and consisted mainly of Caucasian, highly educated arrhythmia
patients, which may not be representative of the general cardiac/arrhythmia population in the
United States. In addition, stringent criteria were used to control for medical and emotional
health. As such, the patients in this sample were psychologically healthier than similar samples
described in the ICD literature (Sears, 2003; Goodeman, 2004; Kuhl, 2006). Given that most of
our hypotheses were based on the prediction of high anxiety, specifically shock-related anxiety,
the lack of shock-specific anxiety in this cohort may have affected the results obtained.
Alternatively, it is possible that the ICD patients as well as AF controls were not anxious enough
(given the low state/trait anxiety scores for both groups) for robust group effects to emerge.
Other similar studies have found individuals with higher STAI scores, specifically after
experimental mood induction (Wilson & MacLeod, 2003; Fox et al, 2005) have biased
attentional processing to emotionally-relevant information.
Another critical limitation that may have affected the ICD cohort in this study is the
changing nature of the technology. More and more patients are being “paced” out of life
threatening arrhythmias. That is, the ICD can detect an abnormally fast heart rhythm and as it
prepares to fire, may terminate the rhythm before it becomes necessary to shock. The ICD group
51
in this study had a low incidence of shock (63% had no shocks), as a result, they may not have
been as anxious regarding their devices nor were they even familiar with post-shock
psychological sequelae. Additionally, those who had been shocked at least once may have been
educated about device acceptance and ICD shock and were therefore less concerned about the
device. The ICD cohort at Shands hospital has been involved in numerous studies over the past
15 years specifically focusing on ICD education and device acceptance. Given the small number
of clinics from which recruitment occurred, it is possible that oversampling of this population
affected their responses on familiar measures of psychosocial effects and device knowledge.
Finally, the present study may have benefited from a post-task questionnaire as well as
post-task ratings of state, trait and shock-related anxiety. Qualitative feedback from participants
regarding their subjective experience during the task may have aided in clarifying inherent
cohort specific problems with the task (e.g. too easy, unclear etc). Post-task anxiety
questionnaires would have offered a data point to examine whether the task itself induced
anxiety in our patients.
Future Directions
Future studies may improve on the present methodology by employing a “classic” dot-
probe paradigm which uses word pairs as cues (McLeod, Mathews, & Tata, 1986). Additionally,
given the low levels of anxiety in this cohort, ICD related mood induction may prove useful in
clarifying effects of device specific anxiety on attentional bias. Mood induction is widely used
in studies examining affective processing and is a powerful tool to induce an affective state
(Compton, 2003). Use of the startle paradigm (e.g. eye-blink reflex) may provide more direct
measurement of heightened threat relevant arousal and vigilance in arrhythmia patients.
Additionally, direct measures of physiological arousal such as skin conductance, heart rate, and
blood pressure may also be useful in characterizing and differentiating VF and AF patients.
52
APPENDIX WORD STIMULI USED IN TASK
Table A-1.Word stimuli used in task Pleasant Neutral Threat (noncardiac) Cardiac Threat Fame Align Agitate Shock Grin Mascot Bully Defibrillate Sunrise Logic Ruin Flutter Delight Depot Curse Fatal Brave Perform Avenge Palpitations Bold Panel Damage Dizzy Affection Prompt Insult Faint Cheer Review Hurt Racing Cute Knit Horrify Heartbeat Kitten Invent Loathe Pain
53
REFERENCES
Algom, D., Chajut, E. & Lev, S. (2004). A rational look at the emotional stroop phenomenon: a generic slowdown, not a stroop effect. Journal of Experimental Psychology, 133, (3), 323-338.
Amir, N., Elias, J., Klumpp, H., & Przeworski, A. (2003). Attentional bias to threat in social
phobia: facilitated processing of threat or difficulty disengaging attention from threat? Behaviour Research and Therapy, 41,1325-1335.
Armony, J.L., & LeDoux, J.E. (2000). How danger is encoded: Towards a systems, cellular and
computational understanding of cognitive-emotional interactions in fear. In M.S. Gazzaniga (Ed.), The new cognitive neurosciences (2nd Ed. pp1067-1079). Boston: MIT Press.
AVID Investigators. (1997). A comparison of antiarrhythmic-drug therapy with implantable
defibrillators in patients resuscitated from near-fatal ventricular arrhythmias. New England Journal of Medicine, 337, 1576-1583.
Beck, A.T. (1976). Cognitive therapy and emotional disorders. New York: International Universities Press.
Beck, A., Steer, R., & Brown, G. (1996). Manual for Beck Depression Inventory-II (2nd ed.).
San Antonio, TX: Psychological Corporation. Beck, J.G., Freeman, J.B., Shipherd, J.C., Hamblen, J.L., Lackner, J.M. (2001). Specificity of
stroop interference in patients with pain and PTSD. Journal of Abnormal Psychology, 110 (4): 536-43.
Bishop, S., Duncan, J., Brett, M., & Lawrence, A.D. (2004). Prefrontal cortical function and
anxiety: controlling attention to threat-related stimuli. Nature Neuroscience, 7(2), 184-188.
Bradley, M.M., & Lang, P.J. (1999). Affective norms for English words (ANEW): Stimuli,
instruction manual and affective ratings. Technical report C-1, Gainesville, FL. The Center for Research in Psychophysiology, University of Florida
Broomfield, N.M. & Turpin, G. (2005). Covert and overt attention in trait anxiety: a cognitive
psychophysiological analysis. Biological Psychology, 68, 179-200.Bush, G., Luu, P., & Posner, M.I. (2000). Cognitive and emotional influences in anterior
cingulated cortex. Trends in Cognitive Sciences, 4 (6), 215-222. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, N.J.:
L. Erlbaum Associates.
54
Cohen, J. D., Botvinick, M., & Carter, C. S. (2000). Anterior cingualte and prefrontal cortex: Who's in control? Nature Neuroscience, 3, 421-423.
Compton, R.J. (2003). The interface between emotion and attention: a review of evidence from
psychology and neuroscience. Behavioral and Cognitive Neuroscience Reviews, 2 (2), 115-129.
Compton, R.J. & Banich, M.T. (2003). Paying attention to emotion: an fMRI investigation of
cognitive and emotional stroop tasks. Cognitive, Affective, & Behavioral Neuroscience, 3 (2), 81-96.
Damasio, A. R. (1994). Descartes’ error. Emotion, reason, and the human brain. New York:
Putnam. Deghani, M., Sharpe, L., & Nicholas, M.K. (2003). Selective attention to pain-related
information in chronic musculoskeletal pain patients. Pain, 105, 37-46. Derryberry, D., & Reed, M.A. (1994). Temperament and attention: orienting toward and away
from positive and negative signals. Journal of Personality and Social Psychology, 66, 1128-1139.
Derryberry, D., & Reed, M.A. (2002). Anxiety-related attentional biases and their regulation by
attentional control. Journal of Abnormal Psychology, 111 (2), 225-236. Dolan, R. (2000). Emotional processing in the human bran revealed through functional
neuroimaging. In M.S. Gazzaniga (Ed). The new cognitive neurosciences (2nd ed. pp. 701-710.) Cambridge, MA: MIT Press.
Dunbar, S.B., Kimble, L.P., Jenkins, L.S., Hawthorne, M., Dudley, W., Slemmons, M., et al.
(1999). Association of mood disturbance and arrhythmia events in patients after cardioverter defibrillator implantation. Depression and Anxiety, 9, 163-168.
Fox, E., Russo, R., Bowles, R., & Dutton, K. (2001). Do threatening stimuli draw or hold visual
attention in subclinical anxiety? Journal of Experimental Psychology: General, 130 (4), 681-700.
Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed
disengagement from emotional faces. Cognition and Emotion, 16, 355-379. Godemann, F., Aherns, B., Behrens, S, Berthold, R., Gandor, C., Lampe, F., Linden, M. (2001)
Classic conditioning and dysfunctional congitions in patients with panic disorder and agoraphobia treated with implantable cardioverter/defibrillator. Psychosomatic Medicine, 63, 231-238.
55
Godemann, F., Butter, C., Lampe, F., Linden, M., Werner, S., & Behrens, S. (2004). Determinants of the quality of life (QoL) in patients with an implantable cardioverter/defibrillator (ICD). Quality of Life Research, 13, 411-416.
Haywood, C. (1995). Psychiatric Illness and cardiovascular disease risk. Epidemiology Review,
17, 129-138.
Hegel, M.T., Griegel, L.E., Black, C., Goulden, L., & Ozahowski, T. (1997). Anxiety and depression in patients receiving implanted cardioverter-defibrillators: A longitudinal investigation. International Journal of Psychiatry in Medicine, 27, 57-69.
Herrman, C., von zur Muhen, F., Schaumann, A., Buss, U., Kemper, S., Wantzen, C., et al.
(1997). Standardized assessment of psychological well-being and quality-of-life in patients with implanted defibrillators. Pacing and Clinical Electrophysiology, 20, 95-103.
Irvine, J., Dorian, P., Baker, B., O’Brien, B.J., Roberts, R., Gent, M., Newman, D., & Connolly,
S.J. (2002). Quality of life in the Canadian Implantable Defibrillator Study (CIDS). American Heart Journal, 144, 282-289.
Kamphuis H.C., de Leeuw J.R., Derksen R., Hauer RN, Winnubst JA. (2003). Implantable
cardioverter defibrillator recipients: quality of life in recipients with and without ICD shock delivery: a prospective study. Europace, 5, 381-389.
Kiernan, R., Mueller, J., Langston, W. & Van Dyke, C., (1987). The Neurobehavioral Cognitive
Status Examination: A brief but differentiated approach to cognitive assessment. Annals of Internal Medicine 107, pp. 481–485
Kubzansky, L.D., Kawachi, I., Weiss, S.T., & Sparrow, D. (1998). Anxiety and coronary heart
disease: a synthesis of epidemiological, psychological, and experimental evidence. Annals of Behavioral Medicine, 20, 47-58.
Kohn, C.S., Pterucci, R.J., Baessler, C., Soto, D.M., & Movsowitz. C. (2000). The effect of
psychological intervention on patients’ long-term adjustment to the ICD: A prospective study. PACE, 23, 450-456.
Koster, E.H.W, Crombez, G., VanDamme, S., Verscheuere, B., & De Houwer, J. (2004). Does
imminent threat capture and hold attention?Emotion, 4, (3), 312-317. Kroeze, S. & van den Hout, M.A. (2000). Selective attention for cardiac information in panic
patients. Behaviour Research and Therapy, 38, 63-72. Lemon J, Edelman S, Kirkness A. (2004). Avoidance behaviors in patients with implantable
cardioverter defibrillators. Heart Lung, 33, 176-82. Lane, R.D., Chua, P.M.-L. & Dolan, R.J. (1997). Neural activation during selective attention to
subjective emotional responses. NeuroReport, 8, 3968-3972.
56
Lang, P.J., Bradley, M.M. & Cuthburt, B.N. (1997). Motivated attention: affect, activation and
action. In P.J. Lang, R.F. Simons, & M.T. Balaban (Eds), Attention and orienting: sensory and motivational process (pp.97-135. Mahwah, NJ: Lawrence Earlbaum.
Lang, P.J., Davis, M., & Ohman A. (2000). Fear and anxiety: animal models and human
cognitive psychophysiology. Journal of Affective Disorders, 61, 137-159.
Le Doux, J. (1996). The emotional brain. New York: Simon & Schuster. Lazarus, R.S. (1966). Psychological Stress and the coping process. New York: McGraw Hill. Mangun, G.R., Jha, A. Hopfinger, J.B. & Handy, T.C. (2000). The temporal dynamics and
functional architechture of attentional processes in human extrastriate cortex. In M.S. Gazzaniga (Ed). The new cognitive neurosciences (2nd ed. pp. 701-710.) Cambridge, MA: MIT Press.
MacLeod, C.M. & MacDonald, P.A. (2000). Interdimensional interference in the stroop effect:
uncovering the cognitive and neural anatomy of attention. Trends in Cognitive Sciences, 4 (10), 383-391.
MacLeod, C., Mathews, A. & Tata, P. (1986). Attentional bias in emotional disorder. Journal of
Abnormal Psychology, 95, 15-20. Mogg K & Bradley, B. (2002). Selective orienting to masked faces in social anxiety.
Behaviour Therapy and Research, 40 (12), 1403-1414.
Moss, A. J., Hall, W. J., Cannom, D. S., Daubert, J. P., Higgins, S. L., Klien, H., et al., for the Multicenter Automatic Defibrillator Implantation Trial Investigators. (1996). Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia. New England Journal of Medicine, 335, 1933-1940.
Oatley, K. & Johnson-Laird, P.N. (1987). Towards a cognitive theory of emotions. Cognition
and Emotion, 1, 29-50. Ohman, A. (1997). As fast as the blink of an eye: evolutionary preparedness for preattentive
processing of threat. In P.J. Lang, R.F. Simons, & M.T. Balaban (Eds), Attention and orienting: sensory and motivational process (pp.97-135. Mahwah, NJ: Lawrence Earlbaum.
O’Leary, C. J., & Jones, P. W. (2000). The left ventricular dysfunction questionnaire (LVD-36):
Reliability, validity, and responsiveness. Heart, 83, 634-640. Pauli, P., Wiedemann, G., Dengler, W., Blaumann-Benninghoff, G., & Kuhlkemp, V. (1999).
Anxiety in patients with an automatic implantable cardioverter defibrillator: What differentiates them from panic patients? Psychosomatic Medicine, 61, 69-76.
57
Pessosa, L. & Ungerleider, L.G. (2004). Neuroimaging studies of attention and the processing of
emotion laden stimuli. Progess in Brain Research, 144, 171-182. Posner, M.I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32,
3-25. Posner, M.I. (1988). Structure and functions of selective attention. In T. Boll & B. Bryant (Eds.),
Clinical neuropsychology and brain function (pp. 173-202). Washington, D.C.: American Psychological Association.
Posner, M.I. & Petersen, S.E. (1990). The attention system of the human brain. Annual Review of
Neuroscience, 13, 25-42. Posner, M.I. & Raichle, M.E. (1994). Images of mind. New York: Scientific American Library. Posner, M.I. (1998). Attention, self-regulartion and consciouness. Philosophical Transcations of
the Royal Society of London B, 353, 1915-1927. Posner, M.I. (2000). Attention in cognitive neuroscience: an overview. In M.S. Gazzaniga (Ed).
The new cognitive neurosciences (2nd ed. pp. 701-710.) Cambridge, MA: MIT Press. Sears, S.F., & Conti, J.B. (2002). Current views on the quality of life and psychological
functioning of implantable cardioverter defibrillator patients. Heart, 87, 488-493. Sears, S.F., & Conti, J.B. (2003). Understanding implantable cardioverter defibrillator shocks
and storms: Medical and psychosocial considerations for research and clinical care. Clinical Cardiology, 26, 107-111.
Sears, S.F., Burns, J.L., Handberg, E. Sotile, W.M., & Conti, J.B. (2001). Young at
heart: Understanding the unique psychosocial adjustment of young implantable cardioverter defibrillator recipients. Journal of Pacing and Clinical Electrophysiology, 24, 1113-1117.
Sears, S.F., Kovacs, A.H., Azzarello, L., Larsen, K., & Conti, J.B. (2004). Innovations in health
psychology: the psychosocial care of adults with implantable cardioverter defibrillators. Professional Psychology Research Practice, 5, 1-7.
Sears, S.F., Kovacs, A.H., Serber, E.R., Kuhl, E.A, Vazquez, L.D., Handberg, E.M., et al.
Psychosocial treatment to optimize quality of life in implantable cardioverter defibrillator patients. In preparation.
Sears, S.F., Todaro, J.F., Lewis, T.S., Sotile, W., & Conti, J.B. (1999). Examining the
psychosocial impact of implantable cardioverter defibrillators: A literature review. Clinical Cardiology, 22, 481-489.
58
Shedd, O., Sears, S.F., Harvill, J.L., Arshad, A., Conti, J.B., Steinberg, J.S. et al. (2004). The World Trade Center attack: Increased frequency of defibrillator shocks for ventricular arrhythmias in patients living remotely from New York City. Journal of the American College of Cardiology, 44, 1265-1267.
Speilberger, C. D., Gorusch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for
the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.
Speilberger, C. D., & Vagg, P. R. (1984). Psychometric properties of the STAI: A reply to Ramanaiah, Franzen, and Schill. Journal of Personality Assessment, 48, 95-97.
Van Damme, S., Lorenz, Jurgen, Eccleston, C., Koser, E.H., De Clercq, A., & Crombez, G.
(2004). Fear-conditioned cues of impending pain facilitate attentional engagement. Clinical Neurophysiology, 34, 33-39.
Ware, J. E., Kosinski, M., & Keller, S. D. (1995). SF-12: How to Score the SF-12 Physical and
Mental Health Scales (2nd ed.). Boston, MA: The Health Institute, New England Medical Center.
Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey:
Construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220-233.
Williams, J. M. G., Watts, F. N., Macleod, C., & Mathews, A. (1997). Cognitive psychology and
emotional disorders (2nd ed.). Chichester, England: Wiley. Wilson, E. & MacLeod, C. (2003). Contrasting two accounts of anxiety-linked attentional bias:
selective attention to varying level of stimulus threat intensity. Journal of Abnormal Psychology, 112, (2), 212-218.
Zajonc, R.B. (2000). Feeling and thinking: closing the debate over the independence of affect. In
J.P. Forgas (Ed), Feeling and thinking: the role of affect in social cognition (pp.31058). Cambridge: Cambridge University Press.
59
BIOGRAPHICAL SKETCH
Neha Dixit graduated from the Mount Holyoke College with a bachelor’s degree in
Neuroscience and Behavior. She then spent 2 years working as a research associate at the
National Institutes of Mental Health, in the Clinical Brain Disorders Branch. Ms. Dixit earned a
masters degree in clinical and health psychology at the University of Florida in 2003 and then
began her doctoral studies in the same program. She concluded her doctoral training with an
internship at the James A. Haley Veteran’s Medical Center in Tampa, FL. After internship, Ms.
Dixit plans on pursuing a neuropsychology post-doctoral position.